Deepfake technology poses escalating threats to enterprises through CEO fraud, reputation attacks, and identity theft. Business leaders need comprehensive strategies to detect and mitigate synthetic media risks.
New research proposes a multi-agent AI reference architecture for securing enterprise AI deployments, addressing governance challenges in managing AI systems at scale.
New research examines how different memory architectures affect LLM agent capabilities, offering insights into designing more effective AI systems.
Researchers assess how well large language models handle questions about recent events, revealing critical limitations in temporal knowledge that affect AI system reliability.
Researchers propose a novel framework for visualizing and benchmarking factual hallucinations in large language models by analyzing internal neural activations and clustering patterns.
Data leakage silently destroys model validity. Learn why preprocessing before splitting contaminates your test set and how to build pipelines that preserve true model performance.
Prompt injection exploits how LLMs process instructions, enabling attackers to hijack AI behavior. Understanding attack vectors and defenses is essential for secure AI deployment.
OpenAI releases research preview of GPT-5.3-Codex-Spark, achieving 15x faster inference with over 1000 tokens per second on Cerebras hardware—a major leap in AI coding capabilities.
Anthropic raises $30 billion in Series G funding, reaching a $380 billion valuation. The massive investment signals continued confidence in frontier AI development.
The European Commission opens formal proceedings against X over Grok AI's generation of explicit deepfake images, marking a significant regulatory action under the Digital Services Act.
ByteDance launches Seedance 2.0, a next-generation AI model that generates video clips from text, images, audio, and video inputs, expanding multimodal capabilities in synthetic media.
New research introduces risk-equalized differentially private synthetic data that protects outliers by controlling record-level influence, addressing critical privacy gaps in AI training data.